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Feasibility studies

Forward modelling of gravity and height based on subsurface models. Data on an observation grid is simulated.
Feasibility model showing gas saturation.

Input data (which a study usually is based on)

  • Geometry of the reservoir or subsurface target
  • Rock properties, such as porosity, fluids, pressures, and their dynamic (time-lapse) changes. Often reservoir flow models contains all required information.
  • Rock stiffness, uniaxial or triaxial, rock or pore compressibility
  • Quantification of model uncertainties or multiple subsurface models
  • Seafloor or surface topography. Geotechnical properties for the foundation of measurement platforms
  • Station layout, or multiple layouts
  • Cost of different components of data aquisition

Forward modelling from subsurface models

  • Calculate geometry of reservoir mass changes from the property model(s). Gravity forward modelling to the surface station grid(s) using Newtons law.
  • Calculate geometry of reservoir heights from the property model(s), usually pore pressure. Deformation forward modelling to the surface station grid(s) using the Geertsma approximation.
 
Feasibility map showing change in mass.
Picture shows a graph of cost-benefit analysis.

Station coverage density (blue line) and indicative value of data (orange line). The largest separation of the curves, at 500-600 m spacing, may give the highest net value. From Eiken and Zumberge (2019).

Simulating surveys

  • Select station grid(s), if not given initially
  • Add noise with realistic level and properties
  • Simulate noisy data with sequence of measurements, drift errors and other correlated noise components
  • Process the simulated data
  • Match with reservoir models or invert for reservoir properties
  • Estimate uncertainties based on mis-matches or inverted differences from initial model
  • Cost estimates for various station grids and survey layouts

Cost-benefit analysis

  • Cost estimates for various station grids and survey layouts
  • Precision of final results for various costs
  • Value of data for different scenarios
  • Cost-benefit analysis

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